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  <title>第 11 章 大叶模型 | 使用 R 语言分析 LI-6400 和 LI-6800 光合仪的数据</title>
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<ul class="summary">
<li><a href="./">R 软件与光合数据分析</a></li>

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<li class="chapter" data-level="" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i>欢迎</a></li>
<li class="chapter" data-level="" data-path="frontmatter.html"><a href="frontmatter.html"><i class="fa fa-check"></i>前言</a></li>
<li class="chapter" data-level="" data-path="copyright.html"><a href="copyright.html"><i class="fa fa-check"></i>版权</a></li>
<li class="chapter" data-level="1" data-path="intro.html"><a href="intro.html"><i class="fa fa-check"></i><b>1</b> R 软件与 Rstudio</a>
<ul>
<li class="chapter" data-level="1.1" data-path="intro.html"><a href="intro.html#rsoft"><i class="fa fa-check"></i><b>1.1</b> R 软件</a></li>
<li class="chapter" data-level="1.2" data-path="intro.html"><a href="intro.html#rstudiosoft"><i class="fa fa-check"></i><b>1.2</b> Rstudio</a></li>
</ul></li>
<li class="chapter" data-level="2" data-path="batch_question.html"><a href="batch_question.html"><i class="fa fa-check"></i><b>2</b> 批量处理光合测定数据</a>
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<li class="chapter" data-level="2.1" data-path="batch_question.html"><a href="batch_question.html#install_readphoto"><i class="fa fa-check"></i><b>2.1</b> 安装</a></li>
<li class="chapter" data-level="2.2" data-path="batch_question.html"><a href="batch_question.html#batch64"><i class="fa fa-check"></i><b>2.2</b> LI-6400 数据处理</a>
<ul>
<li class="chapter" data-level="2.2.1" data-path="batch_question.html"><a href="batch_question.html#li-6400-数据的整合6400combine"><i class="fa fa-check"></i><b>2.2.1</b> LI-6400 数据的整合{#6400combine}</a></li>
<li class="chapter" data-level="2.2.2" data-path="batch_question.html"><a href="batch_question.html#recompute6400"><i class="fa fa-check"></i><b>2.2.2</b> LI-6400 数据重计算</a></li>
</ul></li>
<li class="chapter" data-level="2.3" data-path="batch_question.html"><a href="batch_question.html#li-6800-数据的处理-6800data"><i class="fa fa-check"></i><b>2.3</b> LI-6800 数据的处理 {#6800data}</a>
<ul>
<li class="chapter" data-level="2.3.1" data-path="batch_question.html"><a href="batch_question.html#r-下-excel-格式读取的重计算-6800xlconnect"><i class="fa fa-check"></i><b>2.3.1</b> R 下 Excel 格式读取的重计算 {##6800xlconnect}</a></li>
<li class="chapter" data-level="2.3.2" data-path="batch_question.html"><a href="batch_question.html#python"><i class="fa fa-check"></i><b>2.3.2</b> 使用 Python 来处理</a></li>
<li class="chapter" data-level="2.3.3" data-path="batch_question.html"><a href="batch_question.html#python-r-batch"><i class="fa fa-check"></i><b>2.3.3</b> 批量处理 csv 文件</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="3" data-path="response_fit.html"><a href="response_fit.html"><i class="fa fa-check"></i><b>3</b> CO<sub>2</sub> 响应曲线的拟合</a>
<ul>
<li class="chapter" data-level="3.1" data-path="response_fit.html"><a href="response_fit.html#fvcb_mod"><i class="fa fa-check"></i><b>3.1</b> FvCB 模型</a></li>
<li class="chapter" data-level="3.2" data-path="response_fit.html"><a href="response_fit.html#co2_note"><i class="fa fa-check"></i><b>3.2</b> CO<sub>2</sub> 响应曲线测量的注意事项</a>
<ul>
<li class="chapter" data-level="3.2.1" data-path="response_fit.html"><a href="response_fit.html#model_3"><i class="fa fa-check"></i><b>3.2.1</b> 分段性</a></li>
<li class="chapter" data-level="3.2.2" data-path="response_fit.html"><a href="response_fit.html#note_detail"><i class="fa fa-check"></i><b>3.2.2</b> 测量注意事项</a></li>
</ul></li>
<li class="chapter" data-level="3.3" data-path="response_fit.html"><a href="response_fit.html#plantecophys"><i class="fa fa-check"></i><b>3.3</b> <code>plantecophys</code> 软件包</a></li>
<li class="chapter" data-level="3.4" data-path="response_fit.html"><a href="response_fit.html#fit6400"><i class="fa fa-check"></i><b>3.4</b> LI-6400XT CO<sub>2</sub> 响应曲线的拟合</a>
<ul>
<li class="chapter" data-level="3.4.1" data-path="response_fit.html"><a href="response_fit.html#fitaci_intro"><i class="fa fa-check"></i><b>3.4.1</b> fitaci 函数介绍</a></li>
</ul></li>
<li class="chapter" data-level="3.5" data-path="response_fit.html"><a href="response_fit.html#plantecophy_use"><i class="fa fa-check"></i><b>3.5</b> 使用 <code>plantecophys</code> 拟合 LI-6400XT CO<sub>2</sub> 响应曲线数据</a>
<ul>
<li class="chapter" data-level="3.5.1" data-path="response_fit.html"><a href="response_fit.html#data6400"><i class="fa fa-check"></i><b>3.5.1</b> 数据的前处理</a></li>
<li class="chapter" data-level="3.5.2" data-path="response_fit.html"><a href="response_fit.html#fitaci-p"><i class="fa fa-check"></i><b>3.5.2</b> 使用示例</a></li>
<li class="chapter" data-level="3.5.3" data-path="response_fit.html"><a href="response_fit.html#onpoint_fit"><i class="fa fa-check"></i><b>3.5.3</b> 使用 ‘onepoint’ 单独计算 V<sub>cmax</sub> 和 J<sub>max</sub></a></li>
<li class="chapter" data-level="3.5.4" data-path="response_fit.html"><a href="response_fit.html#multi_curve"><i class="fa fa-check"></i><b>3.5.4</b> 多条 CO<sub>2</sub> 响应曲线的拟合</a></li>
<li class="chapter" data-level="3.5.5" data-path="response_fit.html"><a href="response_fit.html#transition"><i class="fa fa-check"></i><b>3.5.5</b> <code>findCiTransition</code> 函数</a></li>
</ul></li>
<li class="chapter" data-level="3.6" data-path="response_fit.html"><a href="response_fit.html#c4"><i class="fa fa-check"></i><b>3.6</b> C4 植物光合</a>
<ul>
<li class="chapter" data-level="3.6.1" data-path="response_fit.html"><a href="response_fit.html#c4_sim"><i class="fa fa-check"></i><b>3.6.1</b> C4 植物光合速率的计算</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="4" data-path="stomotal_sim.html"><a href="stomotal_sim.html"><i class="fa fa-check"></i><b>4</b> 气孔导度模型的拟合</a>
<ul>
<li class="chapter" data-level="4.1" data-path="stomotal_sim.html"><a href="stomotal_sim.html#ballberry"><i class="fa fa-check"></i><b>4.1</b> BallBerry 模型</a></li>
<li class="chapter" data-level="4.2" data-path="stomotal_sim.html"><a href="stomotal_sim.html#bbleuning"><i class="fa fa-check"></i><b>4.2</b> BBLeuning 模型</a></li>
<li class="chapter" data-level="4.3" data-path="stomotal_sim.html"><a href="stomotal_sim.html#bboptifull"><i class="fa fa-check"></i><b>4.3</b> BBOptiFull 模型</a></li>
<li class="chapter" data-level="4.4" data-path="stomotal_sim.html"><a href="stomotal_sim.html#fitbb-p"><i class="fa fa-check"></i><b>4.4</b> <code>fitBB</code> 函数</a></li>
<li class="chapter" data-level="4.5" data-path="stomotal_sim.html"><a href="stomotal_sim.html#fitbbs"><i class="fa fa-check"></i><b>4.5</b> <code>fitBBs</code> 函数</a></li>
</ul></li>
<li class="chapter" data-level="5" data-path="stomotal_couple.html"><a href="stomotal_couple.html"><i class="fa fa-check"></i><b>5</b> 光合最优气孔导度耦合模型</a>
<ul>
<li class="chapter" data-level="5.1" data-path="stomotal_couple.html"><a href="stomotal_couple.html#farao"><i class="fa fa-check"></i><b>5.1</b> <code>FARAO</code> 函数</a></li>
</ul></li>
<li class="chapter" data-level="6" data-path="photo_stomo.html"><a href="photo_stomo.html"><i class="fa fa-check"></i><b>6</b> 光合气孔导度耦合模型</a>
<ul>
<li class="chapter" data-level="6.1" data-path="photo_stomo.html"><a href="photo_stomo.html#photosyn"><i class="fa fa-check"></i><b>6.1</b> <code>Photosyn</code> 函数</a>
<ul>
<li class="chapter" data-level="6.1.1" data-path="photo_stomo.html"><a href="photo_stomo.html#photo_exam"><i class="fa fa-check"></i><b>6.1.1</b> <code>Photosyn</code> 使用举例</a></li>
</ul></li>
<li class="chapter" data-level="6.2" data-path="photo_stomo.html"><a href="photo_stomo.html#photsyneb"><i class="fa fa-check"></i><b>6.2</b> <code>PhotosynEB</code> 函数</a></li>
<li class="chapter" data-level="6.3" data-path="photo_stomo.html"><a href="photo_stomo.html#photosyntuzet"><i class="fa fa-check"></i><b>6.3</b> <code>PhotosynTuzet</code> 函数</a>
<ul>
<li class="chapter" data-level="6.3.1" data-path="photo_stomo.html"><a href="photo_stomo.html#photosyntuzet_para"><i class="fa fa-check"></i><b>6.3.1</b> <code>PhotosynTuzet</code> 的参数</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="7" data-path="rhtovpd.html"><a href="rhtovpd.html"><i class="fa fa-check"></i><b>7</b> RHtoVPD 函数</a></li>
<li class="chapter" data-level="8" data-path="lrc_fit.html"><a href="lrc_fit.html"><i class="fa fa-check"></i><b>8</b> 光响应曲线的拟合</a>
<ul>
<li class="chapter" data-level="8.1" data-path="lrc_fit.html"><a href="lrc_fit.html#rec_mod"><i class="fa fa-check"></i><b>8.1</b> 直角双曲线模型</a>
<ul>
<li class="chapter" data-level="8.1.1" data-path="lrc_fit.html"><a href="lrc_fit.html#rec_fit"><i class="fa fa-check"></i><b>8.1.1</b> 直角双曲线模型的实现</a></li>
</ul></li>
<li class="chapter" data-level="8.2" data-path="lrc_fit.html"><a href="lrc_fit.html#nonrec-mod"><i class="fa fa-check"></i><b>8.2</b> 非直角双曲线模型</a>
<ul>
<li class="chapter" data-level="8.2.1" data-path="lrc_fit.html"><a href="lrc_fit.html#nonrec_mode_exam"><i class="fa fa-check"></i><b>8.2.1</b> 非直角双曲线模型的实现</a></li>
</ul></li>
<li class="chapter" data-level="8.3" data-path="lrc_fit.html"><a href="lrc_fit.html#lrc_exp"><i class="fa fa-check"></i><b>8.3</b> 指数模型</a>
<ul>
<li class="chapter" data-level="8.3.1" data-path="lrc_fit.html"><a href="lrc_fit.html#lrc_exp_exam"><i class="fa fa-check"></i><b>8.3.1</b> 指数模型的实现</a></li>
</ul></li>
<li class="chapter" data-level="8.4" data-path="lrc_fit.html"><a href="lrc_fit.html#rev_rec"><i class="fa fa-check"></i><b>8.4</b> 直角双曲线的修正模型</a>
<ul>
<li class="chapter" data-level="8.4.1" data-path="lrc_fit.html"><a href="lrc_fit.html#rev_rec_exam"><i class="fa fa-check"></i><b>8.4.1</b> 直角双曲线修正模型的实现</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="9" data-path="start_con.html"><a href="start_con.html"><i class="fa fa-check"></i><b>9</b> 关于非线性拟合的初始值</a>
<ul>
<li class="chapter" data-level="9.1" data-path="start_con.html"><a href="start_con.html#nlslm"><i class="fa fa-check"></i><b>9.1</b> nlsLM 解决方案</a></li>
<li class="chapter" data-level="9.2" data-path="start_con.html"><a href="start_con.html#plot_comp"><i class="fa fa-check"></i><b>9.2</b> 作图比对法</a>
<ul>
<li class="chapter" data-level="9.2.1" data-path="start_con.html"><a href="start_con.html#plot_exam"><i class="fa fa-check"></i><b>9.2.1</b> 实现过程</a></li>
<li class="chapter" data-level="9.2.2" data-path="start_con.html"><a href="start_con.html#show_demo"><i class="fa fa-check"></i><b>9.2.2</b> 直观展示</a></li>
</ul></li>
<li class="chapter" data-level="9.3" data-path="start_con.html"><a href="start_con.html#mult_try"><i class="fa fa-check"></i><b>9.3</b> 自动多次尝试法</a></li>
<li class="chapter" data-level="9.4" data-path="start_con.html"><a href="start_con.html#sum_start"><i class="fa fa-check"></i><b>9.4</b> 小结</a></li>
</ul></li>
<li class="chapter" data-level="10" data-path="anay_6800.html"><a href="anay_6800.html"><i class="fa fa-check"></i><b>10</b> LI-6800 的数据分析</a>
<ul>
<li class="chapter" data-level="10.1" data-path="anay_6800.html"><a href="anay_6800.html#data6800"><i class="fa fa-check"></i><b>10.1</b> 数据格式</a></li>
<li class="chapter" data-level="10.2" data-path="anay_6800.html"><a href="anay_6800.html#dif"><i class="fa fa-check"></i><b>10.2</b> LI-6800 与 LI-6400 使用时的差别</a></li>
<li class="chapter" data-level="10.3" data-path="anay_6800.html"><a href="anay_6800.html#notice"><i class="fa fa-check"></i><b>10.3</b> 光响应曲线注意事项</a></li>
<li class="chapter" data-level="10.4" data-path="anay_6800.html"><a href="anay_6800.html#other_light_response"><i class="fa fa-check"></i><b>10.4</b> 其他软件包的光响应曲线</a></li>
<li class="chapter" data-level="10.5" data-path="anay_6800.html"><a href="anay_6800.html#racir68"><i class="fa fa-check"></i><b>10.5</b> LI-6800 RACiR的测量与拟合</a></li>
<li class="chapter" data-level="10.6" data-path="anay_6800.html"><a href="anay_6800.html#racir-conifer"><i class="fa fa-check"></i><b>10.6</b> LI-6800 RACiR簇状叶的测量与拟合</a></li>
<li class="chapter" data-level="10.7" data-path="anay_6800.html"><a href="anay_6800.html#multi1"><i class="fa fa-check"></i><b>10.7</b> 多个速率的 RACiR 曲线研究</a>
<ul>
<li class="chapter" data-level="10.7.1" data-path="anay_6800.html"><a href="anay_6800.html#multi2"><i class="fa fa-check"></i><b>10.7.1</b> 光呼吸滞后模型</a></li>
<li class="chapter" data-level="10.7.2" data-path="anay_6800.html"><a href="anay_6800.html#code-photoresp"><i class="fa fa-check"></i><b>10.7.2</b> 光呼吸滞后性代码</a></li>
<li class="chapter" data-level="10.7.3" data-path="anay_6800.html"><a href="anay_6800.html#multi4"><i class="fa fa-check"></i><b>10.7.3</b> 数据的构造</a></li>
<li class="chapter" data-level="10.7.4" data-path="anay_6800.html"><a href="anay_6800.html#multi5"><i class="fa fa-check"></i><b>10.7.4</b> 光呼吸滞后性作图</a></li>
<li class="chapter" data-level="10.7.5" data-path="anay_6800.html"><a href="anay_6800.html#multi6"><i class="fa fa-check"></i><b>10.7.5</b> 补偿点计算</a></li>
<li class="chapter" data-level="10.7.6" data-path="anay_6800.html"><a href="anay_6800.html#multi7"><i class="fa fa-check"></i><b>10.7.6</b> 无光呼吸酶失活模块</a></li>
<li class="chapter" data-level="10.7.7" data-path="anay_6800.html"><a href="anay_6800.html#multi9"><i class="fa fa-check"></i><b>10.7.7</b> 酶失活作图</a></li>
<li class="chapter" data-level="10.7.8" data-path="anay_6800.html"><a href="anay_6800.html#multi10"><i class="fa fa-check"></i><b>10.7.8</b> 不同失活程度下补偿点计算</a></li>
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<li class="chapter" data-level="10.8" data-path="anay_6800.html"><a href="anay_6800.html#multi11"><i class="fa fa-check"></i><b>10.8</b> 时间延迟的扩散限制</a>
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<li class="chapter" data-level="10.8.1" data-path="anay_6800.html"><a href="anay_6800.html#multi12"><i class="fa fa-check"></i><b>10.8.1</b> 扩散限制滞后性</a></li>
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<li class="chapter" data-level="10.9" data-path="anay_6800.html"><a href="anay_6800.html#multi13"><i class="fa fa-check"></i><b>10.9</b> 扩散限制作图</a>
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<li class="chapter" data-level="10.9.1" data-path="anay_6800.html"><a href="anay_6800.html#multi14"><i class="fa fa-check"></i><b>10.9.1</b> 补偿点的计算</a></li>
<li class="chapter" data-level="10.9.2" data-path="anay_6800.html"><a href="anay_6800.html#multi15"><i class="fa fa-check"></i><b>10.9.2</b> 所有图形代码</a></li>
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<li class="chapter" data-level="10.10" data-path="anay_6800.html"><a href="anay_6800.html#fluro68"><i class="fa fa-check"></i><b>10.10</b> LI-6800 荧光数据分析</a>
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<li class="chapter" data-level="10.10.1" data-path="anay_6800.html"><a href="anay_6800.html#jiptest"><i class="fa fa-check"></i><b>10.10.1</b> jip test 的实现</a></li>
<li class="chapter" data-level="10.10.2" data-path="anay_6800.html"><a href="anay_6800.html#jiptest_pack"><i class="fa fa-check"></i><b>10.10.2</b> <code>jiptest</code> 软件包安装</a></li>
<li class="chapter" data-level="10.10.3" data-path="anay_6800.html"><a href="anay_6800.html#readfluor"><i class="fa fa-check"></i><b>10.10.3</b> <code>read_files</code> 及 <code>read_dcfiles</code> 函数</a></li>
<li class="chapter" data-level="10.10.4" data-path="anay_6800.html"><a href="anay_6800.html#testfluor"><i class="fa fa-check"></i><b>10.10.4</b> <code>jip_test</code> 及 <code>jip_dctest</code> 函数</a></li>
<li class="chapter" data-level="10.10.5" data-path="anay_6800.html"><a href="anay_6800.html#plotfluor"><i class="fa fa-check"></i><b>10.10.5</b> 图像查看函数</a></li>
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<li class="chapter" data-level="11" data-path="big-leaf.html"><a href="big-leaf.html"><i class="fa fa-check"></i><b>11</b> 大叶模型</a>
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<li class="chapter" data-level="11.1" data-path="big-leaf.html"><a href="big-leaf.html#leaf-scale-meas"><i class="fa fa-check"></i><b>11.1</b> 叶片尺度测量</a></li>
<li class="chapter" data-level="11.2" data-path="big-leaf.html"><a href="big-leaf.html#big-leaf-data"><i class="fa fa-check"></i><b>11.2</b> 数据的处理</a>
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<li class="chapter" data-level="11.2.1" data-path="big-leaf.html"><a href="big-leaf.html#single-data-big-leaf"><i class="fa fa-check"></i><b>11.2.1</b> 单个测量数据的处理</a></li>
<li class="chapter" data-level="11.2.2" data-path="big-leaf.html"><a href="big-leaf.html#big-leaf-data-MODEL"><i class="fa fa-check"></i><b>11.2.2</b> 大叶模型的数据处理</a></li>
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<li class="chapter" data-level="12" data-path="pca-anylysis.html"><a href="pca-anylysis.html"><i class="fa fa-check"></i><b>12</b> 大话 PCA</a>
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<li class="chapter" data-level="12.1" data-path="pca-anylysis.html"><a href="pca-anylysis.html#geom-pca"><i class="fa fa-check"></i><b>12.1</b> 几何解释</a></li>
<li class="chapter" data-level="12.2" data-path="pca-anylysis.html"><a href="pca-anylysis.html#alge-pca"><i class="fa fa-check"></i><b>12.2</b> 线性代数解释</a>
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<li class="chapter" data-level="12.2.1" data-path="pca-anylysis.html"><a href="pca-anylysis.html#egi-pca"><i class="fa fa-check"></i><b>12.2.1</b> 特征向量与特征值</a></li>
<li class="chapter" data-level="12.2.2" data-path="pca-anylysis.html"><a href="pca-anylysis.html#man_pca"><i class="fa fa-check"></i><b>12.2.2</b> 手动实现过程</a></li>
<li class="chapter" data-level="12.2.3" data-path="pca-anylysis.html"><a href="pca-anylysis.html#prcom"><i class="fa fa-check"></i><b>12.2.3</b> <code>prcomp</code> 的实现</a></li>
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<li class="chapter" data-level="13" data-path="sessioninfo.html"><a href="sessioninfo.html"><i class="fa fa-check"></i><b>13</b> 环境与配置</a></li>
<li class="chapter" data-level="" data-path="references.html"><a href="references.html"><i class="fa fa-check"></i>参考文献</a></li>
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          <h1>
            <i class="fa fa-circle-o-notch fa-spin"></i><a href="./">使用 R 语言分析 LI-6400 和 LI-6800 光合仪的数据</a>
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<div id="big-leaf" class="section level1" number="11">
<h1><span class="header-section-number">第 11 章</span> 大叶模型</h1>
<p>题目中所提到的大叶模型，并非指用涡动数据来进行大尺度的直接测量后的大叶模型 <span class="citation">(<a href="#ref-knauerBigleafPackageCalculation2018" role="doc-biblioref">Knauer et al. 2018</a>)</span>，是指 <span class="citation">Mejdová et al. (<a href="#ref-mejdovaPhotosyntheticParametersSedgegrass2021" role="doc-biblioref">2021</a>)</span> 使用 LI-6400 和 LI-3000C 的方式进行的个体叶片尺度的光合测量，外推到整个群落尺度的大叶模型，这也是一个比较新的测量方法，发表在 Scientific Reports。这里对其方法进行概述，因为所用方法是之前早都提过的基本方式。</p>
<div id="leaf-scale-meas" class="section level2" number="11.1">
<h2><span class="header-section-number">11.1</span> 叶片尺度测量</h2>
<p>文章中所使用的是 LI-6400，但毫无疑问，LI-6800 可以更好的胜任该工作，具体测量方法为：</p>
<ol style="list-style-type: decimal">
<li>从 4 月中旬到 10 月，除 6 月因为洪水有中断外，每周进行一次光响应曲线的测量。</li>
<li>为最小化几个优势种测量时样本之间的变异性，考虑了枝条的差异、枝条不同叶片之间的年龄、以及环境的随机效应的影响。测量选取的是一系列相邻的枝条或草丛。在特定的日期，每个植株选择两个枝条上的，在完全展开的成熟叶片中选择最 2-4 最年轻的叶片进行测量（2片或4片，由物种而定）。</li>
<li>测量的不为距离叶片顶端约 3/4，选择的都是冠层顶部的叶片。</li>
<li>测量过程是标准的光响应曲线的测量流程，不同的是在光强设置为 0 时，额外等待 4 min，用于测量暗呼吸速率。</li>
<li>该实验测量的时间是欧洲中部时间的 7-11 点。</li>
</ol>
</div>
<div id="big-leaf-data" class="section level2" number="11.2">
<h2><span class="header-section-number">11.2</span> 数据的处理</h2>
<div id="single-data-big-leaf" class="section level3" number="11.2.1">
<h3><span class="header-section-number">11.2.1</span> 单个测量数据的处理</h3>
<ol style="list-style-type: decimal">
<li>单个测量的数据使用的是非线性拟合，选取的为非直角双曲线模型，使用了 <code>nls2</code> 作为非线性拟合的工具。</li>
<li>暗呼吸速率的值是拟合模型曲线与纵坐标的交点。
3.不同物种、不同测量日期和不同生育期的参数各自拟合。</li>
</ol>
</div>
<div id="big-leaf-data-MODEL" class="section level3" number="11.2.2">
<h3><span class="header-section-number">11.2.2</span> 大叶模型的数据处理</h3>
<ol style="list-style-type: decimal">
<li>在不同季节，使用 LI-3100C 测量了不同物种的叶面积指数。</li>
<li>计算单个植株的 LAI 占不同时期以及整个生长季 LAI 的比例，以此作为该植株拟合参数的权重。而整个大叶模型的参数则是对这些权重数据进行求和。然后将这些参数带入所用的非直角双曲线模型。</li>
</ol>
<div class="figure" style="text-align: center"><span style="display:block;" id="fig:lai-big-leaf"></span>
<img src="images/weighted-parameter.png" alt="根据 LAI 权重求和来计算大叶模型的参数" width="564" />
<p class="caption">
图 11.1: 根据 LAI 权重求和来计算大叶模型的参数
</p>
</div>
<p>光响应曲线的拟合可以参考 <a href="lrc_fit.html#nonrec-mod">8.2</a>。部分实验结果如下：</p>
<div class="figure" style="text-align: center"><span style="display:block;" id="fig:lai-big-leaf-model"></span>
<img src="images/big-leaf-model.png" alt="假定不同物种组成下的模型结果" width="322" />
<p class="caption">
图 11.2: 假定不同物种组成下的模型结果
</p>
</div>

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<h3>参考文献</h3>
<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-knauerBigleafPackageCalculation2018" class="csl-entry">
Knauer, Jürgen, Tarek S. El-Madany, Sönke Zaehle, and Mirco Migliavacca. 2018. <span>“Bigleaf—<span>An</span> <span>R</span> Package for the Calculation of Physical and Physiological Ecosystem Properties from Eddy Covariance Data.”</span> <em>PLOS ONE</em> 13 (8): e0201114. <a href="https://doi.org/10.1371/journal.pone.0201114">https://doi.org/10.1371/journal.pone.0201114</a>.
</div>
<div id="ref-mejdovaPhotosyntheticParametersSedgegrass2021" class="csl-entry">
Mejdová, Markéta, Jiří Dušek, Lenka Foltýnová, Lenka Macálková, and Hana Čížková. 2021. <span>“Photosynthetic Parameters of a Sedge-Grass Marsh as a Big-Leaf: Effect of Plant Species Composition.”</span> <em>Scientific Reports</em> 11 (1): 3723. <a href="https://doi.org/10.1038/s41598-021-82382-2">https://doi.org/10.1038/s41598-021-82382-2</a>.
</div>
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